SUMMARY Many biological processes occur not at the level of a cell but at the level of a system, and cell-cell interactions are crucial for tissue function. With the introduction of single-cell RNA-Seq, we have robust measures of cell types and cell states. In this approach however, the tissue under study must be dissociated prior to sequencing resulting in the loss of spatial context. Spatial transcriptomics is a promising new field, in which several methods have been developed to profile the transcriptome of cells in their native context. However, the most widely used implementation of this technology – sequencing-based spatial transcriptomics – has not reached single-cell resolution. Thus, there is a critical need for novel computational approaches integrating spatial transcriptomics and single-cell RNA-Seq in order to infer cell-cell relationships in complex tissues. Our lab has recently developed analyses for multimodal intersection of these two data sources that effectively mitigate the limitations of each technology. Here, we propose to apply this concept to uncover patterns of cell-cell interactions in biological systems. In our first Aim, we present the StateMap approach to infer local cell-cell interactions by spatial transcriptomics-based co-localization and receptor-ligand relationships. A catalog of cell types and cell states is first delineated using single-cell data, and the spatial transcriptomics data is then harnessed to map pairs of co-localizing cell states. StateMap then systematically infers the cell-cell interaction mechanisms among co-localizing cell states by statistically testing for signal/response relationships in the spatial transcriptomics data. In our second Aim, we propose the ST-motif method to conceptualize the locations of cell types and states as a network, allowing for systematic analysis by a wealth of available methods. Our approach thus reframes the problem of finding cell-cell relationships as a network motif problem in this graph. Throughout our proposal, we develop and test the algorithms on two model systems, the male germline and the placenta, with which our lab has considerable experience. Conceptually, our proposal promises to yield novel algorithms for mapping cell-cell interactions that are required for actuating the potential of two powerful transcriptomic technologies.